Abnormality identification assist system and learning method

Abstract

An abnormality identification assist system includes an artificial intelligence processor, at least one processor, and a storage medium. The artificial intelligence processor is configured to use a malfunction code output from a vehicle as input and to infer an abnormal part of the vehicle in which an abnormality is occurring and a category of the abnormality. The storage medium is configured to store a program configured to be executed by the at least one processor. The program includes at least one command configured to cause the at least one processor to execute processing for inputting the malfunction code output from the vehicle into the artificial intelligence processor and instructing the artificial intelligence processor to infer the abnormal part in which the abnormality is occurring and the category of the abnormality corresponding to the input malfunction code.

1 . An abnormality identification assist system comprising: 
 an artificial intelligence processor configured to use a malfunction code output from a vehicle as input and to infer an abnormal part of the vehicle in which an abnormality is occurring and a category of the abnormality;   at least one processor; and   a storage medium configured to store a program configured to be executed by the at least one processor,   wherein the program includes at least one command configured to cause the at least one processor to execute processing for inputting the malfunction code output from the vehicle into the artificial intelligence processor and instructing the artificial intelligence processor to infer the abnormal part in which the abnormality is occurring and the category of the abnormality corresponding to the input malfunction code.   
     
     
         2 . The abnormality identification assist system according to  claim 1 , wherein the at least one command is configured to cause the at least one processor to execute inference result display processing for causing a display to display information indicating an inference result obtained by the artificial intelligence processor. 
     
     
         3 . The abnormality identification assist system according to  claim 2 , wherein, in the inference result display processing, the at least one processor is configured to cause the display to display information indicating candidates for each of the abnormal part and the category of the abnormality which are obtained by the artificial intelligence processor as the inference result. 
     
     
         4 . The abnormality identification assist system according to  claim 1 , wherein: 
 the artificial intelligence processor comprises   a first artificial intelligence model configured to learn to infer the category of the abnormality from the malfunction code output from the vehicle as a result of conducting machine learning by using the malfunction code as input data and the category of the abnormality as supervisor data, and   a second artificial intelligence model configured to learn to infer the abnormal part from a combination of the malfunction code output from the vehicle and the category of the abnormality as a result of conducting machine learning by using the malfunction code and the category of the abnormality as the input data and the abnormal part as the supervisor data; and   the first artificial intelligence model infers the category of the abnormality corresponding to the input malfunction code, which is a malfunction code input as the input data, while the second artificial intelligence model infers the abnormal part by using the input malfunction code and the category of the abnormality inferred by the first artificial intelligence model as the input data.   
     
     
         5 . The abnormality identification assist system according to  claim 4 ,  
       wherein the category of the abnormality comprises a plurality of categories of abnormalities to be inferred by the first artificial intelligence mode, and the abnormal part comprises a plurality of abnormal parts to be inferred by the second artificial intelligence model, and 
       wherein the categories of abnormalities are smaller in number than the abnormal parts. 
     
     
         6 . A learning method comprising: 
 conducting machine learning for artificial intelligence by using a malfunction code output from a vehicle as input data for learning and by using an abnormal part of the vehicle in which an abnormality has occurred and a category of the abnormality as supervisor data; and   generating artificial intelligence configured to infer the abnormal part and the category of the abnormality corresponding to the input malfunction code.